ROOT.gStyle.SetOptTitle(0)
from utils import get_hists
from rplot.rdir import Rdir
fnames = [f[0]['file'] for f in rfiles]
rpath_tool = Rdir(fnames)
def _filter(string):
matches = ['MVA_{}{}'.format(cl, string) for cl in classifiers]
return lambda k: k.GetName() in matches
rocs = []
for i, rfileconf in enumerate(rfiles):
# roc curve: MVA_<name>_rejBvsS
roc = get_hists(classifiers, rfileconf, rpath_tool, robj_t=ROOT.TH1,
robj_p=_filter('_rejBvsS'))
roc = dict((k, v[0]) for k, v in roc.iteritems()) # cleanup dict
rocs.append(roc)
# config
axis_range = options.axis_range
doprint = options.doprint
if options.usempl:
# Matplotlib
import matplotlib.pyplot as plt
plt.rc('font', family='Liberation Sans') # choose font
plt.rc('mathtext', default='regular') # use default font for math
# ROOT to Matplotlib translation layer
import rootpy.plotting.root2matplotlib as rplt
if not fnmatchcase(key, clnameglob): del classifiers[key]
from fixes import ROOT
if batch: ROOT.gROOT.SetBatch(True)
from utils import get_hists
from rplot.rplot import Rplot, arrange
def _filter(string):
matches = ['MVA_{}{}'.format(cl, string) for cl in classifiers]
return lambda k: k.GetName() in matches
_filter1 = lambda string: lambda k: _filter(string+'_S')(k) or _filter(string+'_B')(k)
_filter2 = lambda str1, str2: lambda k: _filter1(str1)(k) or _filter1(str2)(k)
distribs = get_hists(classifiers, rfileconf, rpath_tool,
robj_t = ROOT.TH1, robj_p = _filter2('', '_Train'))
if rarity:
rarity = get_hists(classifiers, rfileconf, rpath_tool,
robj_t = ROOT.TH1, robj_p = _filter1('_Rarity'))
if probab:
probab = get_hists(classifiers, rfileconf, rpath_tool,
robj_t = ROOT.TH1, robj_p = _filter1('_Proba'))
plotter = Rplot(1, 1, 800, 600)
plotter.alpha = 0.2
plotter.fill_colours = (ROOT.kAzure, ROOT.kRed, ROOT.kAzure, ROOT.kRed)
plotter.line_colours = (ROOT.kAzure-6, ROOT.kRed+2, ROOT.kAzure-6, ROOT.kRed+2)
plotter.markers = (ROOT.kPlus, ROOT.kPlus, ROOT.kPlus, ROOT.kPlus)
canvas = plotter.prep_canvas()
if doprint: canvas.Print('overtraining.pdf[')
from rplot.rdir import Rdir
fnames = [f[0]['file'] for f in rfiles]
rpath_tool = Rdir(fnames)
def _filter(string):
matches = ['MVA_{}{}'.format(cl, string) for cl in classifiers]
return lambda k: k.GetName() in matches
rocs = []
for i, rfileconf in enumerate(rfiles):
# roc curve: MVA_<name>_rejBvsS
roc = get_hists(classifiers,
rfileconf,
rpath_tool,
robj_t=ROOT.TH1,
robj_p=_filter('_rejBvsS'))
roc = dict((k, v[0]) for k, v in roc.iteritems()) # cleanup dict
rocs.append(roc)
# config
axis_range = options.axis_range
doprint = options.doprint
if options.usempl:
# Matplotlib
import matplotlib.pyplot as plt
plt.rc('font', family='Liberation Sans') # choose font
plt.rc('mathtext', default='regular') # use default font for math
for key in transforms:
if not fnmatchcase(key, options.transglob):
del transforms[key]
from fixes import ROOT
ROOT.gROOT.SetBatch(options.batch)
ROOT.gStyle.SetOptTitle(options.title)
from utils import get_hists
from rplot.rplot import Rplot, arrange, partition
## variable distributions
if options.distribs:
# histogram order: signal, background (repeat for diff transforms)
distributions = get_hists(transforms, rfileconf, pathtool, robj_t=ROOT.TH1)
ROOT.gStyle.SetHatchesLineWidth(1)
ROOT.gStyle.SetHatchesSpacing(2.5)
for transform in transforms:
def _style(l):
# l.reverse()
l[0].SetFillStyle(3345)
l[1].SetFillStyle(3354)
distributions[transform] = arrange(distributions[transform], 2,
predicate=_style)
plotter = Rplot(3, 3, 2000, 1200)
plotter.alpha = 0.2
canvas = plotter.prep_canvas()
if options.doprint:
from utils import get_hists
from rplot.rplot import Rplot, arrange
def _filter(string):
matches = ['MVA_{}{}'.format(cl, string) for cl in classifiers]
return lambda k: k.GetName() in matches
_filter1 = lambda string: lambda k: _filter(string + '_S')(k) or _filter(
string + '_B')(k)
_filter2 = lambda str1, str2: lambda k: _filter1(str1)(k) or _filter1(str2)(k)
distribs = get_hists(classifiers,
rfileconf,
rpath_tool,
robj_t=ROOT.TH1,
robj_p=_filter2('', '_Train'))
if options.rarity:
rarity = get_hists(classifiers,
rfileconf,
rpath_tool,
robj_t=ROOT.TH1,
robj_p=_filter1('_Rarity'))
else:
rarity = None
if options.probab:
probab = get_hists(classifiers,
rfileconf,
rpath_tool,
robj_t=ROOT.TH1,
